Yuan Lin, Lai Jinling, Zhao Jing, Sun Tao, Hu Chunyu, Ye Lan, Yu Guanying, Yang Zhenyu
School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
Cancer Center, The Second Hospital of Shandong University, Jinan, China.
Front Genet. 2022 Jun 16;13:896884. doi: 10.3389/fgene.2022.896884. eCollection 2022.
Attention convolutional neural networks (ATT-CNNs) have got a huge gain in picture operating and nature language processing. Shortage of interpretability cannot remain the adoption of deep neural networks. It is very conspicuous that is shown in the prediction model of disease aftermath. Biological data are commonly revealed in a nominal grid data structured pattern. ATT-CNN cannot be applied directly. In order to figure out these issues, a novel method which is called the Path-ATT-CNN is proposed by us, making an explicable ATT-CNN model based on united omics data by making use of a recently characterized pathway image. Path-ATT-CNN shows brilliant predictive demonstration difference in primary lung tumor symptom (PLTS) and non-primary lung tumor symptom (non-PLTS) when applied to lung adenocarcinomas (LADCs). The imaginational tool adoption which is linked with statistical analysis enables the status of essential pathways which finally exist in LADCs. In conclusion, Path-ATT-CNN shows that it can be effectively put into use elucidating omics data in an interpretable mode. When people start to figure out key biological correlates of disease, this mode makes promising power in predicting illness.
注意力卷积神经网络（ATT-CNNs）在图像操作和自然语言处理方面取得了巨大进展。缺乏可解释性阻碍了深度神经网络的应用。这在疾病预后预测模型中表现得非常明显。生物数据通常以名义网格数据结构模式呈现。ATT-CNN不能直接应用。为了解决这些问题，我们提出了一种名为Path-ATT-CNN的新方法，通过利用最近表征的通路图像，基于联合组学数据构建一个可解释的ATT-CNN模型。当应用于肺腺癌（LADCs）时，Path-ATT-CNN在原发性肺肿瘤症状（PLTS）和非原发性肺肿瘤症状（非PLTS）方面显示出出色的预测性能差异。与统计分析相关联的想象工具的采用能够确定最终存在于LADCs中的关键通路的状态。总之，Path-ATT-CNN表明它可以有效地以可解释的方式用于阐明组学数据。当人们开始找出疾病的关键生物学关联时，这种模式在预测疾病方面具有巨大潜力。
